Overview
There is mainly 3 parts to this story:
A simple easy to communicate model of the key relationship
A medium complexity smoothing analysis
A full power time series analysis with causal inference
The two data set used in this analysis are the Madison case and waste water concentration data.
## Date Site Cases N1 N1Error
## 435 2021-06-19 Madison 3 NA NA
## 436 2021-06-20 Madison 8 49443 8812
## 437 2021-06-21 Madison 1 90447 20429
## 438 2021-06-22 Madison 3 44587 12427
## 439 2021-06-23 Madison 4 14469 9155
## 440 2021-06-24 Madison NA 28023 7724
A simple display of the data shows the core components of this story. First that both data sets are extremely noisy. And that there is a hint of a relationship between the two signals.
MinMaxNorm <- function(Vec){#normalizes the data to range from 0 and 1
normVec <- (Vec-min(Vec,na.rm=TRUE))/max(Vec,na.rm=TRUE)
return(normVec)
}
NoNa <- function(DF,...){#Removes NA from the two reverent columns
ColumnNames <- c(...)
print(ColumnNames)
NoNaDF <- DF%>%
filter(
across(
.cols = ColumnNames,
.fns = ~ !is.na(.x))
)
return(NoNaDF)
}
FirstImpression <- FullDF%>%
NoNa("N1","Cases")%>%#Removing outliers
ggplot(aes(x=Date))+#Data depends on time
geom_line(aes(y=MinMaxNorm(N1), color="N1",info=N1))+#compares N1 to Cases
geom_line(aes(y=MinMaxNorm(Cases), color="Cases",info=Cases))+
labs(y="variable min max normalized")
## [1] "N1" "Cases"
ggplotly(FirstImpression,tooltip=c("info","Date"))
From a first pass it is clear that the waste water measurements before 11/20/2020 did not function as an effective measure of the amount of waste water shed in the community. So for this analysis we are removing waste water data from before that point. Also there are some extreme outliers that we remove for more effective analysis
## [1] "N1" "Cases"
A key component to this relationship is that the relationship between N1 and Case involves a gamma distribution modeling both the time between catching Covid-19 and getting a test and the concentration of the shedded particles. We found a gamma distribution with mean 11.73 days and a sd of 7.68 match’s other research and gives good results.
scale <- 5.028338
shape <- 2.332779 #These parameters are equivilent to the mean and sd above
weights <- dgamma(1:21, scale = scale, shape = shape)
plot(weights,
main=paste("Gamma Distribution with mean = 11.73 days,and SD = 7.68"),
ylab = "Weight",
xlab = "Lag")
SLDSmoothedDF <- FullDF3%>%
mutate(SLDCases = c(rep(NA,20),
rollapply(Cases,width=21,FUN=weighted.mean,
w=weights,
na.rm = TRUE)))
SLDSmoothedDF%>%
NoNa("N1","SLDCases")%>%
ggplot(aes(x=Date))+
geom_line(aes(y=MinMaxNorm(SLDCases), color="SLDCases"))+
geom_line(aes(y=MinMaxNorm(N1), color="N1"))+
facet_wrap(~Site)+
labs(y="variable min max normalized")
## [1] "N1" "SLDCases"
To isolate this relationship we used a primitive binning relationship. This clarifies the relationship we see hints of in the previous graphic and masks the noise in the data.
medianMean <- function(Vec){
return(mean(replace(Vec, c(which.min(Vec), which.max(Vec)), NA), na.rm = TRUE))
}
StartDate <- 1
DaySmoothing <- 14
Lag <- 4
BinDF <- SLDSmoothedDF%>%
select(Date, SLDCases, N1)%>%
mutate(MovedCases = data.table::shift(SLDCases, Lag),
Week=(as.numeric(Date)+StartDate)%/%DaySmoothing)%>%
group_by(Week)%>%
#filter(Week>2670)%>%
summarise(BinnedCases=mean(MovedCases, na.rm=TRUE), BinnedN1=exp(mean(log(N1), na.rm=TRUE)))
BinDF%>%
ggplot()+
geom_line(aes(x=Week, y=MinMaxNorm(BinnedN1), color="N1"))+
geom_line(aes(x=Week, y=MinMaxNorm(BinnedCases), color="Cases"))+
labs(y="Binned variable min max normalized")
BinDF%>%
ggplot()+
geom_point(aes(x=BinnedCases, y=BinnedN1))
cor(BinDF$BinnedN1, BinDF$BinnedCases, use="pairwise.complete.obs")
## [1] 0.8941983
summary(lm(BinnedCases~BinnedN1, data=BinDF))
##
## Call:
## lm(formula = BinnedCases ~ BinnedN1, data = BinDF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.903 -32.241 -5.098 5.512 87.714
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.329e+01 1.888e+01 -1.234 0.236
## BinnedN1 8.530e-04 1.103e-04 7.736 1.3e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 41.93 on 15 degrees of freedom
## (16 observations deleted due to missingness)
## Multiple R-squared: 0.7996, Adjusted R-squared: 0.7862
## F-statistic: 59.85 on 1 and 15 DF, p-value: 1.298e-06
To generate this relationship without reducing the amount of data we rely on a Loess smoothing of the data. The first step is to get a more rigorous measure of the relationship of the data. Here we will use cross correlation. These figures sup port the work done in previous steps.
ccf(SLDSmoothedDF$Cases, SLDSmoothedDF$N1, na.action=na.pass)
ccf(SLDSmoothedDF$SLDCases, SLDSmoothedDF$N1, na.action=na.pass)
ccf(BinDF$BinnedCases, BinDF$BinnedN1, na.action=na.pass)
The loess smoothing is a way of generating smooth curves from noisy data. The displayed plot shows the visual power of this smoothing. We see a relationship in the big patterns but also multiple sub patterns match. We see in general that smoothed N1 both lags and leads the case data.
SLDSmoothedDF$loessN1 <- loessFit(y=(SLDSmoothedDF$N1),
x=SLDSmoothedDF$Date,
span=.2,
iterations=3)$fitted
SLDSmoothedDF%>%
NoNa("loessN1","SLDCases")%>%
ggplot()+
geom_line(aes(x=Date, y=MinMaxNorm(loessN1), color="loessN1"))+
geom_line(aes(x=Date, y=MinMaxNorm(SLDCases), color="SLDCases"))+
facet_wrap(~Site)+
labs(y="variable min max normalized")
## [1] "loessN1" "SLDCases"
ccf(SLDSmoothedDF$loessN1, SLDSmoothedDF$SLDCases, na.action=na.pass)
input <- initialize data
loess
SLD smooth
Analysis <- plot
Analysis <- CCF